Identifying tumor cells at the single-cell level using machine learning | Genome Biology
Tumors are complex tissues of cancerous cells surrounded by a heterogeneous cellular microenvironment with which they interact. Here, researchers propose ikarus, a machine learning pipeline aimed at distinguishing tumor cells from normal cells at the single-cell level.
A spectrum of free software tools for processing the VCF variant call format: vcflib, bio-vcf, cyvcf2, hts-nim and slivar | PLOS Computational Biology
Since its introduction in 2011 the variant call format (VCF) has been widely adopted for processing DNA and RNA variants in practically all population studies—as well as in somatic and germline mutation studies. Here the authors present a spectrum of over 125 useful, complimentary free and open source software tools and libraries, they wrote and made available through the multiple vcflib, bio-vcf, cyvcf2, hts-nim and slivar projects. These tools are applied for comparison, filtering, normalisation, smoothing and annotation of VCF, as well as output of statistics, visualisation, and transformations of files variants.
ScanNet uncovers binding motifs in protein structures with deep learning | Nature Methods
Determining the functional properties of a protein from its structure is challenging. This study presents an interpretable deep learning model that directly learns function-bearing structural motifs from raw data, allowing accurate mapping of protein binding sites and antibody epitopes onto a protein structure.